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Zhang B, Huang S, Zhou C, Zhu J, Chen T, Feng S, Huang C, Wang Z, Wu S, Liu C, Zhan X. Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods. Comput Assist Surg (Abingdon) 2024; 29:2345066. [PMID: 38860617 DOI: 10.1080/24699322.2024.2345066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. METHODS The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. RESULTS For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). CONCLUSION We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.
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Affiliation(s)
- Bin Zhang
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Orthopaedics, The Guizhou Hospital of Beijing Jishuitan Hospital, Guiyang, China
| | - Shengsheng Huang
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chenxing Zhou
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jichong Zhu
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Tianyou Chen
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Sitan Feng
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chengqian Huang
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zequn Wang
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shaofeng Wu
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chong Liu
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xinli Zhan
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Martín Vicario C, Rodríguez Salas D, Maier A, Hock S, Kuramatsu J, Kallmuenzer B, Thamm F, Taubmann O, Ditt H, Schwab S, Dörfler A, Muehlen I. Uncertainty-aware deep learning for trustworthy prediction of long-term outcome after endovascular thrombectomy. Sci Rep 2024; 14:5544. [PMID: 38448445 PMCID: PMC10917742 DOI: 10.1038/s41598-024-55761-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/27/2024] [Indexed: 03/08/2024] Open
Abstract
Acute ischemic stroke (AIS) is a leading global cause of mortality and morbidity. Improving long-term outcome predictions after thrombectomy can enhance treatment quality by supporting clinical decision-making. With the advent of interpretable deep learning methods in recent years, it is now possible to develop trustworthy, high-performing prediction models. This study introduces an uncertainty-aware, graph deep learning model that predicts endovascular thrombectomy outcomes using clinical features and imaging biomarkers. The model targets long-term functional outcomes, defined by the three-month modified Rankin Score (mRS), and mortality rates. A sample of 220 AIS patients in the anterior circulation who underwent endovascular thrombectomy (EVT) was included, with 81 (37%) demonstrating good outcomes (mRS ≤ 2). The performance of the different algorithms evaluated was comparable, with the maximum validation under the curve (AUC) reaching 0.87 using graph convolutional networks (GCN) for mRS prediction and 0.86 using fully connected networks (FCN) for mortality prediction. Moderate performance was obtained at admission (AUC of 0.76 using GCN), which improved to 0.84 post-thrombectomy and to 0.89 a day after stroke. Reliable uncertainty prediction of the model could be demonstrated.
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Affiliation(s)
- Celia Martín Vicario
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany.
- Pattern Recognition Lab, Friedrich Alexander University, Erlangen, Germany.
| | - Dalia Rodríguez Salas
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
- Pattern Recognition Lab, Friedrich Alexander University, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich Alexander University, Erlangen, Germany
| | - Stefan Hock
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Joji Kuramatsu
- Department of Neurology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Bernd Kallmuenzer
- Department of Neurology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | | | | | | | - Stefan Schwab
- Department of Neurology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Iris Muehlen
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
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Park S, Choi J, Kim Y, You JSH. Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation. NeuroRehabilitation 2024; 54:619-628. [PMID: 38943406 DOI: 10.3233/nre-240070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2024]
Abstract
BACKGROUND Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait training (RAGT) in individuals with hemiparetic stroke undergoing such intervention remains unexplored. OBJECTIVE We aimed to determine the best predictive model based on the international classification of functioning impairment domain features (Fugl- Meyer assessment (FMA), Modified Barthel index related-gait scale (MBI), Berg balance scale (BBS)) and reveal their responsiveness to robotic assisted gait training (RAGT) in patients with subacute stroke. METHODS Data from 187 people with subacute stroke who underwent a 12-week Walkbot RAGT intervention were obtained and analyzed. Overall, 18 potential predictors encompassed demographic characteristics and the baseline score of functional and structural features. Five predictive ML models, including decision tree, random forest, eXtreme Gradient Boosting, light gradient boosting machine, and categorical boosting, were used. RESULTS The initial and final BBS, initial BBS, final Modified Ashworth scale, and initial MBI scores were important features, predicting functional improvements. eXtreme Gradient Boosting demonstrated superior performance compared to other models in predicting functional recovery after RAGT in patients with subacute stroke. CONCLUSION eXtreme Gradient Boosting may be an invaluable prognostic tool, providing clinicians and caregivers with a robust framework to make precise clinical decisions regarding the identification of optimal responders and effectively pinpoint those who are most likely to derive maximum benefits from RAGT interventions.
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Affiliation(s)
- Seonmi Park
- Department of Physical Therapy, Sports·Movement Artificial-Intelligence Robotics Technology (SMART) Institute, Yonsei University, Wonju, South Korea
| | - Jongeun Choi
- Department of Mechanical Engineering, Machine Learning and Control Systems Lab, Yonsei University, South Korea
| | - Yonghoon Kim
- Chungdam Rehabilitation Hospital Center, Seoul, South Korea
| | - Joshua Sung H You
- Department of Physical Therapy, Sports·Movement Artificial-Intelligence Robotics Technology (SMART) Institute, Yonsei University, Wonju, South Korea
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Chang YC, Lin HF, Chen YF, Chen HY, Shiu YT, Shi HY. Minimal Clinically Important Difference (MCID) in the Functional Status Measures in Patients with Stroke: Inverse Probability Treatment Weighting. J Clin Med 2023; 12:5828. [PMID: 37762771 PMCID: PMC10532241 DOI: 10.3390/jcm12185828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/16/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
This study proposed to evaluate the temporal trend, define the minimal clinically important difference (MCID) for five functional status measures, and identify risk factors for reaching deterioration in the MCID. This prospective cohort study analyzed 680 patients with ischemic stroke and 151 patients with hemorrhagic stroke at six hospitals between April 2015 and October 2021. All patients completed the functional status measures before rehabilitation (baseline), and at the 12th week and 2nd year after rehabilitation. Patients in the post-acute care (PAC) group exhibited significantly larger improvements for the functional status measures compared to those in the non-PAC group (p < 0.05). Patients with hemorrhagic stroke also displayed larger improvements in the functional status measures when compared to patients with ischemic stroke. Furthermore, the improvement in MCID ranged from 0.01 to 16.18 points when comparing baseline and the 12th week after rehabilitation, but the deterioration in MCID ranged from 0.38 to 16.12 points. Simultaneously, assessing the baseline and the second year after rehabilitation, the improvement in MCID ranged from 0.01 to 18.43 points, but the deterioration in MCID ranged from 0.68 to 17.26 points. Additionally, the PAC program, age, education level, body mass index, smoking, readmission within 30 days, baseline functional status score, use of Foley catheter and nasogastric tube, as well as a history of previous stroke are significantly associated with achieving deterioration in MCID (p < 0.05). These findings suggest that if the mean change scores of the functional status measures have reached the thresholds, the change scores can be perceived by patients as clinically important.
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Affiliation(s)
- Yu-Chien Chang
- Division of Neurology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung 80249, Taiwan;
| | - Hsiu-Fen Lin
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan;
- Department of Neurology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yu-Fu Chen
- Department of Clinical Education & Research, Yuan’s General Hospital, Kaohsiung 80249, Taiwan;
| | - Hong-Yaw Chen
- Superintendent and Division of Digestive Surgery, Department of Surgery, Yuan’s General Hospital, Kaohsiung 80249, Taiwan;
| | - Yu-Tsz Shiu
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
| | - Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Business Management, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
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Chen YW, Li YC, Huang CY, Lin CJ, Tien CJ, Chen WS, Chen CL, Lin KC. Predicting Arm Nonuse in Individuals with Good Arm Motor Function after Stroke Rehabilitation: A Machine Learning Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4123. [PMID: 36901133 PMCID: PMC10001502 DOI: 10.3390/ijerph20054123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Many stroke survivors demonstrate arm nonuse despite good arm motor function. This retrospective secondary analysis aims to identify predictors of arm nonusers with good arm motor function after stroke rehabilitation. A total of 78 participants were categorized into 2 groups using the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE) and the Motor Activity Log Amount of Use (MAL-AOU). Group 1 comprised participants with good motor function (FMA-UE ≥ 31) and low daily upper limb use (MAL-AOU ≤ 2.5), and group 2 comprised all other participants. Feature selection analysis was performed on 20 potential predictors to identify the 5 most important predictors for group membership. Predictive models were built with the five most important predictors using four algorithms. The most important predictors were preintervention scores on the FMA-UE, MAL-Quality of Movement, Wolf Motor Function Test-Quality, MAL-AOU, and Stroke Self-Efficacy Questionnaire. Predictive models classified the participants with accuracies ranging from 0.75 to 0.94 and areas under the receiver operating characteristic curve ranging from 0.77 to 0.97. The result indicates that measures of arm motor function, arm use in activities of daily living, and self-efficacy could predict postintervention arm nonuse despite good arm motor function in stroke. These assessments should be prioritized in the evaluation process to facilitate the design of individualized stroke rehabilitation programs to reduce arm nonuse.
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Affiliation(s)
- Yu-Wen Chen
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
- Department of Speech Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, 365, Mingde Road, Taipei 112, Taiwan
| | - Yi-Chun Li
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
- Department of Occupational Therapy, I-Shou University College of Medicine, 8, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung 824, Taiwan
| | - Chien-Yu Huang
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
| | - Chia-Jung Lin
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
| | - Chia-Jui Tien
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
| | - Wen-Shiang Chen
- Department of Physical Medicine and Rehabilitation, College of Medicine, National Taiwan University, Taipei 10048, Taiwan
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei 10048, Taiwan
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan District, Miaoli 350, Taiwan
| | - Chia-Ling Chen
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, 5 Fusing Street, Gueishan District, Taoyuan 333, Taiwan
- Graduate Institute of Early Intervention, College of Medicine, Chang Gung University, 259 Wenhua 1st Road, Gueishan District, Taoyuan 333, Taiwan
| | - Keh-Chung Lin
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
- Division of Occupational Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei 100, Taiwan
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Chen YW, Lin KC, Li YC, Lin CJ. Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study. J Neuroeng Rehabil 2023; 20:25. [PMID: 36823626 PMCID: PMC9948491 DOI: 10.1186/s12984-023-01151-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 02/14/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measures for activities and participation in stroke rehabilitation requires further investigation. METHODS This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient-reported outcome measures of daily activities (i.e, the Motor Activity Log and the Nottingham Extended Activities of Daily Living) and participation (i.e, the Activities of Daily Living domain of the Stroke Impact Scale). Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k-nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection. RESULTS Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper-limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best-performing models with 18 features ranged between 0.72 and 0.96. Those of the best-performing models with fewer features ranged between 0.72 and 0.84. CONCLUSIONS Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient-reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient-centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation.
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Affiliation(s)
- Yu-Wen Chen
- grid.19188.390000 0004 0546 0241School of Occupational Therapy, College of Medicine, National Taiwan University, 17, F4, Xuzhou Rd., Taipei, Taiwan ,grid.412146.40000 0004 0573 0416Department of Speech Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Keh-chung Lin
- grid.19188.390000 0004 0546 0241School of Occupational Therapy, College of Medicine, National Taiwan University, 17, F4, Xuzhou Rd., Taipei, Taiwan ,grid.412094.a0000 0004 0572 7815Division of Occupational Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, 7 Chung-Shan S. Rd., Taipei, Taiwan
| | - Yi-chun Li
- grid.19188.390000 0004 0546 0241School of Occupational Therapy, College of Medicine, National Taiwan University, 17, F4, Xuzhou Rd., Taipei, Taiwan ,grid.411447.30000 0004 0637 1806Department of Occupational Therapy, I-Shou University College of Medicine, Kaohsiung, Taiwan
| | - Chia-Jung Lin
- grid.19188.390000 0004 0546 0241School of Occupational Therapy, College of Medicine, National Taiwan University, 17, F4, Xuzhou Rd., Taipei, Taiwan
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Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis. Biomolecules 2023; 13:biom13020334. [PMID: 36830703 PMCID: PMC9953156 DOI: 10.3390/biom13020334] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/30/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
The administration of thrombolysis usually reduces the risk of death and the consequences of stroke in the acute phase. However, having received thrombolysis administration is not a prognostic factor for neurorehabilitation outcome in the subacute phase of stroke. It is conceivably due to the complex intertwining of many clinical factors. An artificial neural network (ANN) analysis could be helpful in identifying the prognostic factors of neurorehabilitation outcomes and assigning a weight to each of the factors considered. This study hypothesizes that the prognostic factors could be different between patients who received and those who did not receive thrombolytic treatment, even if thrombolysis is not a prognostic factor per se. In a sample of 862 patients with ischemic stroke, the tested ANN identified some common factors (such as disability at admission, age, unilateral spatial neglect), some factors with higher weight in patients who received thrombolysis (hypertension, epilepsy, aphasia, obesity), and some other factors with higher weight in the other patients (dysphagia, malnutrition, total arterial circulatory infarction). Despite the fact that thrombolysis is not an independent prognostic factor for neurorehabilitation, it seems to modify the relative importance of other clinical factors in predicting which patients will better respond to neurorehabilitation.
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Bian R, Huo M, Liu W, Mansouri N, Tanglay O, Young I, Osipowicz K, Hu X, Zhang X, Doyen S, Sughrue ME, Liu L. Connectomics underlying motor functional outcomes in the acute period following stroke. Front Aging Neurosci 2023; 15:1131415. [PMID: 36875697 PMCID: PMC9975347 DOI: 10.3389/fnagi.2023.1131415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023] Open
Abstract
Objective Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify markers of functional outcomes. Methods Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke. Sixteen models were constructed to predict performance across six tests of motor impairment, spasticity, and activities of daily living, using either whole-brain structural or functional connectivity. Feature importance analysis was also performed to identify brain regions and networks associated with performance in each test. Results The area under the receiver operating characteristic curve ranged from 0.650 to 0.868. Models utilizing functional connectivity tended to have better performance than those utilizing structural connectivity. The Dorsal and Ventral Attention Networks were among the top three features in several structural and functional models, while the Language and Accessory Language Networks were most commonly implicated in structural models. Conclusions Our study highlights the potential of machine learning methods combined with connectivity analysis in predicting outcomes in neurorehabilitation and disentangling the neural correlates of functional impairments, though further longitudinal studies are necessary.
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Affiliation(s)
- Rong Bian
- Department of Rehabilitation, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ming Huo
- University of Health and Rehabilitation Sciences, Qingdao, China
| | - Wan Liu
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Xiaorong Hu
- Xijia Medical Technology Company Limited, Shenzhen, China
| | - Xia Zhang
- Xijia Medical Technology Company Limited, Shenzhen, China.,International Joint Research Center on Precision Brain Medicine, Xidian Group Hospital, Xi'an, China
| | | | - Michael E Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia.,International Joint Research Center on Precision Brain Medicine, Xidian Group Hospital, Xi'an, China
| | - Li Liu
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Buyandelger B, Chen YW, Li YC, Lin CJ, Chen CL, Lin KC. Predictors for Upper-Limb Functional Recovery Trajectory in Individuals Receiving Stroke Rehabilitation: A Secondary Analysis of Data from Randomized Controlled Trials. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16514. [PMID: 36554396 PMCID: PMC9778967 DOI: 10.3390/ijerph192416514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND The objective of the study was to determine predictors for upper-limb functional recovery trajectory after occupational therapy in a population with chronic stroke. METHODS In this retrospective secondary analysis, Fugl-Meyer Assessment-Upper Extremity (FMA-UE) scores before and after intervention and at the 3-month follow-up were used to divide 105 participants with chronic stroke into three groups of recovery trajectories: fast (participants who reached an improvement of 7 after intervention), extended (those who reached an improvement of 7 at follow-up), and limited (those who did not reach an improvement of 7) recovery. Comparisons among the three groups were made in demographics, stroke characteristics, and baseline assessment scores. Logistic regression analyses were performed to determine predictors for group membership. RESULTS Time after onset of stroke and the baseline scores of FMA-UE, Stroke Impact Scale-Hand (SIS-Hand), Wolf Motor Function Test (WMFT)-Quality, WMFT-Time scores, Motor Activity Log-Amount of Use (MAL-AOU), and Motor Activity Log-Quality of Movement (MAL-QOM) scores were significantly different among the three groups. Univariate logistic regressions confirmed that SIS-Hand, WMFT-Quality, WMFT-Time, MAL-AOU, and MAL-QOM were significant predictors for both the fast versus limited recovery group membership and the extended versus limited group membership. Time after stroke onset and baseline FMA-UE were additional predictors for the fast versus limited recovery group membership. CONCLUSION These findings may assist healthcare professionals in making optimal therapeutic decisions and in informing clients and caregivers about the outcomes of stroke recovery.
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Affiliation(s)
- Batsaikhan Buyandelger
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
| | - Yu-Wen Chen
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
| | - Yi-Chun Li
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
| | - Chia-Jung Lin
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
| | - Chia-Ling Chen
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, 5 Fusing Street, Gueishan District, Taoyuan 333, Taiwan
- Graduate Institute of Early Intervention, College of Medicine, Chang Gung University, 259 Wenhua 1st Road, Gueishan District, Taoyuan 333, Taiwan
| | - Keh-Chung Lin
- School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan
- Division of Occupational Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei 100, Taiwan
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Cerasa A, Tartarisco G, Bruschetta R, Ciancarelli I, Morone G, Calabrò RS, Pioggia G, Tonin P, Iosa M. Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics. Biomedicines 2022; 10:biomedicines10092267. [PMID: 36140369 PMCID: PMC9496389 DOI: 10.3390/biomedicines10092267] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/29/2022] Open
Abstract
Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke and traumatic brain injury (TBI). Thirteen papers directly addressing the different performance among ML and LR methods were included in this review. Basically, ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury. Better performance of specific ML algorithms (such as Artificial neural networks) was mainly described in the stroke domain, but the high heterogeneity in features extracted from low-dimensional clinical data reduces the enthusiasm for applying this powerful method in clinical practice. To better capture and predict the dynamic changes in patients with brain injury during intensive care courses ML algorithms should be extended to high-dimensional data extracted from neuroimaging (structural and fMRI), EEG and genetics.
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Affiliation(s)
- Antonio Cerasa
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Rende, Italy
- S. Anna Institute, 88900 Crotone, Italy
- Correspondence:
| | - Gennaro Tartarisco
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
| | - Roberta Bruschetta
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Irene Ciancarelli
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
- San Raffaele Sulmona Institute, 67039 Sulmona, Italy
| | | | - Giovanni Pioggia
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
| | | | - Marco Iosa
- IRCCS Centro Neurolesi “Bonino-Pulejo”, 98123 Messina, Italy
- Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy
- Santa Lucia Foundation IRCSS, 00179 Rome, Italy
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11
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Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke. Sci Rep 2022; 12:11235. [PMID: 35787657 PMCID: PMC9253044 DOI: 10.1038/s41598-022-14986-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 06/16/2022] [Indexed: 12/04/2022] Open
Abstract
Health related quality of life (HRQOL) reflects individuals perceived of wellness in health domains and is often deteriorated after stroke. Precise prediction of HRQOL changes after rehabilitation interventions is critical for optimizing stroke rehabilitation efficiency and efficacy. Machine learning (ML) has become a promising outcome prediction approach because of its high accuracy and easiness to use. Incorporating ML models into rehabilitation practice may facilitate efficient and accurate clinical decision making. Therefore, this study aimed to determine if ML algorithms could accurately predict clinically significant HRQOL improvements after stroke sensorimotor rehabilitation interventions and identify important predictors. Five ML algorithms including the random forest (RF), k-nearest neighbors (KNN), artificial neural network, support vector machine and logistic regression were used. Datasets from 132 people with chronic stroke were included. The Stroke Impact Scale was used for assessing multi-dimensional and global self-perceived HRQOL. Potential predictors included personal characteristics and baseline cognitive/motor/sensory/functional/HRQOL attributes. Data were divided into training and test sets. Tenfold cross-validation procedure with the training data set was used for developing models. The test set was used for determining model performance. Results revealed that RF was effective at predicting multidimensional HRQOL (accuracy: 85%; area under the receiver operating characteristic curve, AUC-ROC: 0.86) and global perceived recovery (accuracy: 80%; AUC-ROC: 0.75), and KNN was effective at predicting global perceived recovery (accuracy: 82.5%; AUC-ROC: 0.76). Age/gender, baseline HRQOL, wrist/hand muscle function, arm movement efficiency and sensory function were identified as crucial predictors. Our study indicated that RF and KNN outperformed the other three models on predicting HRQOL recovery after sensorimotor rehabilitation in stroke patients and could be considered for future clinical application.
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12
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Ma X, Zhang Z. Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4219976. [PMID: 35789608 PMCID: PMC9250440 DOI: 10.1155/2022/4219976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 05/30/2022] [Indexed: 11/25/2022]
Abstract
Objective The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent.
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Affiliation(s)
- Xiaojun Ma
- School of Physical Education, South China University of Technology, Guangzhou 510640, Guangdong, China
| | - Zhenfeng Zhang
- Zhenzhou University of Aeronautics, Zhenzhou 450046, Hena, China
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13
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Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke. SENSORS 2022; 22:s22041374. [PMID: 35214276 PMCID: PMC8963097 DOI: 10.3390/s22041374] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/21/2022] [Accepted: 02/09/2022] [Indexed: 02/01/2023]
Abstract
Many recent studies have highlighted that the harmony of physiological walking is based on a specific proportion between the durations of the phases of the gait cycle. When this proportion is close to the so-called golden ratio (about 1.618), the gait cycle assumes an autosimilar fractal structure. In stroke patients this harmony is altered, but it is unclear which factor is associated with the ratios between gait phases because these relationships are probably not linear. We used an artificial neural network to determine the weights associable to each factor for determining the ratio between gait phases and hence the harmony of walking. As expected, the gait ratio obtained as the ratio between stride duration and stance duration was found to be associated with walking speed and stride length, but also with hip muscle forces. These muscles could be important for exploiting the recovery of energy typical of the pendular mechanism of walking. Our study also highlighted that the results of an artificial neural network should be associated with a reliability analysis, being a non-deterministic approach. A good level of reliability was found for the findings of our study.
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14
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Qiu Y, Zheng Y, Liu Y, Luo W, Du R, Liang J, Yilifate A, You Y, Jiang Y, Zhang J, Chen A, Zhang Y, Huang S, Wang B, Ou H, Lin Q. Synergistic Immediate Cortical Activation on Mirror Visual Feedback Combined With a Soft Robotic Bilateral Hand Rehabilitation System: A Functional Near Infrared Spectroscopy Study. Front Neurosci 2022; 16:807045. [PMID: 35185457 PMCID: PMC8855034 DOI: 10.3389/fnins.2022.807045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/12/2022] [Indexed: 11/23/2022] Open
Abstract
Background Mirror visual feedback (MVF) has been widely used in neurological rehabilitation. Due to the potential gain effect of the MVF combination therapy, the related mechanisms still need be further analyzed. Methods Our self-controlled study recruited 20 healthy subjects (age 22.150 ± 2.661 years) were asked to perform four different visual feedback tasks with simultaneous functional near infrared spectroscopy (fNIRS) monitoring. The right hand of the subjects was set as the active hand (performing active movement), and the left hand was set as the observation hand (static or performing passive movement under soft robotic bilateral hand rehabilitation system). The four VF tasks were designed as RVF Task (real visual feedback task), MVF task (mirror visual feedback task), BRM task (bilateral robotic movement task), and MVF + BRM task (Mirror visual feedback combined with bilateral robotic movement task). Results The beta value of the right pre-motor cortex (PMC) of MVF task was significantly higher than the RVF task (RVF task: -0.015 ± 0.029, MVF task: 0.011 ± 0.033, P = 0.033). The beta value right primary sensorimotor cortex (SM1) in MVF + BRM task was significantly higher than MVF task (MVF task: 0.006 ± 0.040, MVF + BRM task: 0.037 ± 0.036, P = 0.016). Conclusion Our study used the synchronous fNIRS to compare the immediate hemodynamics cortical activation of four visual feedback tasks in healthy subjects. The results showed the synergistic gain effect on cortical activation from MVF combined with a soft robotic bilateral hand rehabilitation system for the first time, which could be used to guide the clinical application and the future studies.
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Affiliation(s)
- Yaxian Qiu
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuxin Zheng
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yawen Liu
- Department of Rehabilitation, Guangzhou Medical University, Guangzhou, China
| | - Wenxi Luo
- Department of Rehabilitation, Guangzhou Medical University, Guangzhou, China
| | - Rongwei Du
- Department of Rehabilitation, Guangzhou Medical University, Guangzhou, China
| | - Junjie Liang
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Anniwaer Yilifate
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yaoyao You
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yongchun Jiang
- Department of Rehabilitation, Guangzhou Medical University, Guangzhou, China
| | - Jiahui Zhang
- Department of Rehabilitation, Guangzhou Medical University, Guangzhou, China
| | - Aijia Chen
- Department of Rehabilitation, Guangzhou Medical University, Guangzhou, China
| | - Yanni Zhang
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Siqi Huang
- Department of Rehabilitation, Guangzhou Medical University, Guangzhou, China
| | - Benguo Wang
- Department of Rehabilitation, Longgang District People’s Hospital of Shenzhen, Shenzhen, China
- Department of Rehabilitation, The Third Affiliated Hospital of The Chinese University of Hong Kong, Shenzhen, China
| | - Haining Ou
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Haining Ou,
| | - Qiang Lin
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Qiang Lin,
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15
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Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9028580. [PMID: 35103057 PMCID: PMC8800616 DOI: 10.1155/2022/9028580] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/09/2021] [Accepted: 11/27/2021] [Indexed: 11/26/2022]
Abstract
Customer churn prediction is one of the challenging problems and paramount concerns for telecommunication industries. With the increasing number of mobile operators, users can switch from one mobile operator to another if they are unsatisfied with the service. Marketing literature states that it costs 5–10 times more to acquire a new customer than retain an existing one. Hence, effective customer churn management has become a crucial demand for mobile communication operators. Researchers have proposed several classifiers and boosting methods to control customer churn rate, including deep learning (DL) algorithms. However, conventional classification algorithms follow an error-based framework that focuses on improving the classifier's accuracy over cost sensitization. Typical classification algorithms treat misclassification errors equally, which is not applicable in practice. On the contrary, DL algorithms are computationally expensive as well as time-consuming. In this paper, a novel class-dependent cost-sensitive boosting algorithm called AdaBoostWithCost is proposed to reduce the churn cost. This study demonstrates the empirical evaluation of the proposed AdaBoostWithCost algorithm, which consistently outperforms the discrete AdaBoost algorithm concerning telecom churn prediction. The key focus of the AdaBoostWithCost classifier is to reduce false-negative error and the misclassification cost more significantly than the AdaBoost.
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16
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Mishra S, Tripathy HK, Kumar Thakkar H, Garg D, Kotecha K, Pandya S. An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment. Front Public Health 2021; 9:795007. [PMID: 34976936 PMCID: PMC8718454 DOI: 10.3389/fpubh.2021.795007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 11/10/2021] [Indexed: 11/20/2022] Open
Abstract
Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health.
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Affiliation(s)
- Sushruta Mishra
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | - Hrudaya Kumar Tripathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | | | - Deepak Garg
- Department of Computer Science and Engineering, School of Engineering and Sciences, Bennett University, Greater Noida, India
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbosis International (Deemed) University, Pune, India
| | - Sharnil Pandya
- Symbiosis Institute of Technology, Symbosis International (Deemed) University, Pune, India
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17
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Picerno P, Iosa M, D'Souza C, Benedetti MG, Paolucci S, Morone G. Wearable inertial sensors for human movement analysis: a five-year update. Expert Rev Med Devices 2021; 18:79-94. [PMID: 34601995 DOI: 10.1080/17434440.2021.1988849] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings. AREAS COVERED Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings. EXPERT OPINION IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.
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Affiliation(s)
- Pietro Picerno
- SMART Engineering Solutions & Technologies (SMARTEST) Research Center, Università Telematica "Ecampus", Novedrate, Comune, Italy
| | - Marco Iosa
- Department of Psychology, Sapienza University, Rome, Italy.,Irrcs Santa Lucia Foundation, Rome, Italy
| | - Clive D'Souza
- Center for Ergonomics, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA.,Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Maria Grazia Benedetti
- Physical Medicine and Rehabilitation Unit, IRCCS-Istituto Ortopedico Rizzoli, Bologna, Italy
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18
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Kim B, Schweighofer N, Haldar JP, Leahy RM, Winstein CJ. Corticospinal Tract Microstructure Predicts Distal Arm Motor Improvements in Chronic Stroke. J Neurol Phys Ther 2021; 45:273-281. [PMID: 34269747 PMCID: PMC8460613 DOI: 10.1097/npt.0000000000000363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE The corticospinal tract (CST) is a crucial brain pathway for distal arm and hand motor control. We aimed to determine whether a diffusion tensor imaging (DTI)-derived CST metric predicts distal upper extremity (UE) motor improvements in chronic stroke survivors. METHODS We analyzed clinical and neuroimaging data from a randomized controlled rehabilitation trial. Participants completed clinical assessments and neuroimaging at baseline and clinical assessments 4 months later, postintervention. Using univariate linear regression analysis, we determined the linear relationship between the DTI-derived CST fractional anisotropy asymmetry (FAasym) and the percentage of baseline change in log-transformed average Wolf Motor Function Test time for distal items (ΔlnWMFT-distal_%). The least absolute shrinkage and selection operator (LASSO) linear regressions with cross-validation and bootstrapping were used to determine the relative weighting of CST FAasym, other brain metrics, clinical outcomes, and demographics on distal motor improvement. Logistic regression analyses were performed to test whether the CST FAasym can predict clinically significant UE motor improvement. RESULTS lnWMFT-distal significantly improved at the group level. Baseline CST FAasym explained 26% of the variance in ΔlnWMFT-distal_%. A multivariate LASSO model including baseline CST FAasym, age, and UE Fugl-Meyer explained 39% of the variance in ΔlnWMFT-distal_%. Further, CST FAasym explained more variance in ΔlnWMFT-distal_% than the other significant predictors in the LASSO model. DISCUSSION AND CONCLUSIONS CST microstructure is a significant predictor of improvement in distal UE motor function in the context of an UE rehabilitation trial in chronic stroke survivors with mild-to-moderate motor impairment.Video Abstract available for more insight from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A350).
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Affiliation(s)
- Bokkyu Kim
- Department of Physical Therapy Education, SUNY Upstate Medical University, Syracuse, NY, United States
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
| | - Nicolas Schweighofer
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States
- Brain and Creativity Institute, University of Southern California, Los Angeles, CA, United States
| | - Richard M. Leahy
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States
- Brain and Creativity Institute, University of Southern California, Los Angeles, CA, United States
| | - Carolee J. Winstein
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
- Department. of Neurology, University of Southern California, Los Angeles, CA, United States
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19
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Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses. Brain Sci 2021; 11:brainsci11091147. [PMID: 34573168 PMCID: PMC8466358 DOI: 10.3390/brainsci11091147] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 08/25/2021] [Accepted: 08/27/2021] [Indexed: 11/17/2022] Open
Abstract
There is a large body of literature reporting the prognostic factors for a positive outcome of neurorehabilitation performed in the subacute phase of stroke. Despite the recent development of algorithms based on neural networks or cluster analysis for the identification of these prognostic factors, the literature lacks a rigorous comparison among classical regression, neural network, and cluster analysis. Moreover, the three methods have rarely been tested on a sample independent from that in which prognostic factors have been identified. This study aims at providing this comparison on a wide sample of data (1522 patients) and testing the results on an independent sample (1000 patients) using 30 variables. The accuracy was similar among regression, neural network, and cluster analyses on the analyzed sample (76.6%, 74%, and 76.1%, respectively), but on the test sample, the accuracy of neural network decreased (70.1%). The three models agreed in identifying older age, severe impairment, unilateral spatial neglect, and total anterior circulation infarcts as important prognostic factors. The binary regression analysis also provided solid results in the test sample, especially in terms of specificity (81.8%). Cluster analysis also showed a high sensitivity in the test sample (82.6%) and allowed a meaningful easy-to-use classification tree to be obtained.
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20
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Iosa M, Capodaglio E, Pelà S, Persechino B, Morone G, Antonucci G, Paolucci S, Panigazzi M. Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work. Front Neurol 2021; 12:650542. [PMID: 34093396 PMCID: PMC8170310 DOI: 10.3389/fneur.2021.650542] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 03/23/2021] [Indexed: 11/13/2022] Open
Abstract
A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke.
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Affiliation(s)
- Marco Iosa
- Department of Psychology, Sapienza University of Rome, Rome, Italy.,Scientific Institute for Research, Hospitalization and Healthcare (IRCCS) Santa Lucia Foundation, Rome, Italy
| | - Edda Capodaglio
- Occupational Therapy and Ergonomics Unit, Istituti Clinici Scientifici Maugeri IRCSS, Pavia, Italy
| | - Silvia Pelà
- Occupational Therapy and Ergonomics Unit, Istituti Clinici Scientifici Maugeri IRCSS, Pavia, Italy
| | - Benedetta Persechino
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers' Compensation Authority (INAIL), Rome, Italy
| | - Giovanni Morone
- Scientific Institute for Research, Hospitalization and Healthcare (IRCCS) Santa Lucia Foundation, Rome, Italy
| | - Gabriella Antonucci
- Department of Psychology, Sapienza University of Rome, Rome, Italy.,Scientific Institute for Research, Hospitalization and Healthcare (IRCCS) Santa Lucia Foundation, Rome, Italy
| | - Stefano Paolucci
- Scientific Institute for Research, Hospitalization and Healthcare (IRCCS) Santa Lucia Foundation, Rome, Italy
| | - Monica Panigazzi
- Occupational Therapy and Ergonomics Unit, Istituti Clinici Scientifici Maugeri IRCSS, Pavia, Italy.,Occupational Therapy and Ergonomics Unit, Istituti Clinici Scientifici Maugeri IRCSS, Montescano, Italy
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